



"""simple docstring"""







import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


UpperCamelCase     =							logging.getLogger(__name__)

def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[Any]=2,	UpperCAmelCase_ :       List[str]=3,	UpperCAmelCase_ :       List[str]=16,	UpperCAmelCase_ :       int = 10,	UpperCAmelCase_ :       int = 2     )	-> int:






	"""simple docstring"""





	def get_dataset(UpperCAmelCase_ :       Optional[Any]     ):
		A__											=					torch.randn(batch_size * n_batches,	1     )
		return TensorDataset(UpperCAmelCase_,	a * x + b + 0.1 * torch.randn(batch_size * n_batches,	1     )     )

	A__											=					get_dataset(UpperCAmelCase_     )
	A__											=					get_dataset(UpperCAmelCase_     )
	A__											=					DataLoader(UpperCAmelCase_,	shuffle=UpperCAmelCase_,	batch_size=UpperCAmelCase_,	num_workers=4     )
	A__											=					DataLoader(UpperCAmelCase_,	shuffle=UpperCAmelCase_,	batch_size=UpperCAmelCase_,	num_workers=4     )
	return (train_dataloader, valid_dataloader)

def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       Tuple,	UpperCAmelCase_ :       List[Any],	UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       Dict=None     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					[]
	for epoch in range(UpperCAmelCase_     ):
		# Train quickly
		model.train()
		for batch in dataloader:
			A__							,							A__											=					batch
			A__											=					model(UpperCAmelCase_     )
			A__											=					torch.nn.functional.mse_loss(UpperCAmelCase_,	UpperCAmelCase_     )
			accelerator.backward(UpperCAmelCase_     )
			optimizer.step()
			optimizer.zero_grad()
		rands.append(random.random()     )  # Introduce some randomness
		if scheduler is not None:
			scheduler.step()
	return rands






class       UpperCamelCase__			(   nn.Module      ):

			"""simple docstring"""


			def __init__(      self		)      ->	int:
				super().__init__()
				A__											=					nn.Parameter(torch.randn(1		)		)
				A__											=					nn.Parameter(torch.randn(1		)		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				return x * self.a + self.b






class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	str:
				with tempfile.TemporaryDirectory() as tmpdir:
					set_seed(42		)
					A__											=					DummyModel()
					A__											=					torch.optim.Adam(params=model.parameters()						,	lr=1e-3		)
					A__							,							A__											=					dummy_dataloaders()
					A__											=					ProjectConfiguration(total_limit=1						,	project_dir=SCREAMING_SNAKE_CASE__						,	automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__		)
					# Train baseline
					A__											=					Accelerator(project_config=SCREAMING_SNAKE_CASE__		)
					A__							,							A__							,							A__							,							A__											=					accelerator.prepare(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					# Save initial
					accelerator.save_state()

					# Save second state
					accelerator.save_state()
					self.assertEqual(len(os.listdir(accelerator.project_dir		)		)						,	1		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				with tempfile.TemporaryDirectory() as tmpdir:
					set_seed(42		)
					A__											=					DummyModel()
					A__											=					torch.optim.Adam(params=model.parameters()						,	lr=1e-3		)
					A__							,							A__											=					dummy_dataloaders()
					# Train baseline
					A__											=					Accelerator()
					A__							,							A__							,							A__							,							A__											=					accelerator.prepare(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					# Save initial
					A__											=					os.path.join(SCREAMING_SNAKE_CASE__						,	"initial"		)
					accelerator.save_state(SCREAMING_SNAKE_CASE__		)
					((A__)							,							(A__))											=					model.a.item(), model.b.item()
					A__											=					optimizer.state_dict()
					A__											=					train(3						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					((A__)							,							(A__))											=					model.a.item(), model.b.item()
					A__											=					optimizer.state_dict()

					# Train partially
					set_seed(42		)
					A__											=					DummyModel()
					A__											=					torch.optim.Adam(params=model.parameters()						,	lr=1e-3		)
					A__							,							A__											=					dummy_dataloaders()
					A__											=					Accelerator()
					A__							,							A__							,							A__							,							A__											=					accelerator.prepare(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					accelerator.load_state(SCREAMING_SNAKE_CASE__		)
					((A__)							,							(A__))											=					model.a.item(), model.b.item()
					A__											=					optimizer.state_dict()
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

					A__											=					train(2						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					# Save everything
					A__											=					os.path.join(SCREAMING_SNAKE_CASE__						,	"checkpoint"		)
					accelerator.save_state(SCREAMING_SNAKE_CASE__		)

					# Load everything back in and make sure all states work
					accelerator.load_state(SCREAMING_SNAKE_CASE__		)
					test_rands += train(1						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					((A__)							,							(A__))											=					model.a.item(), model.b.item()
					A__											=					optimizer.state_dict()
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				with tempfile.TemporaryDirectory() as tmpdir:
					set_seed(42		)
					A__											=					DummyModel()
					A__											=					torch.optim.Adam(params=model.parameters()						,	lr=1e-3		)
					A__							,							A__											=					dummy_dataloaders()
					A__											=					ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__		)

					# Train baseline
					A__											=					Accelerator(project_dir=SCREAMING_SNAKE_CASE__						,	project_config=SCREAMING_SNAKE_CASE__		)
					A__							,							A__							,							A__							,							A__											=					accelerator.prepare(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					# Save initial
					accelerator.save_state()
					((A__)							,							(A__))											=					model.a.item(), model.b.item()
					A__											=					optimizer.state_dict()
					A__											=					train(3						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					((A__)							,							(A__))											=					model.a.item(), model.b.item()
					A__											=					optimizer.state_dict()

					# Train partially
					set_seed(42		)
					A__											=					DummyModel()
					A__											=					torch.optim.Adam(params=model.parameters()						,	lr=1e-3		)
					A__							,							A__											=					dummy_dataloaders()
					A__											=					ProjectConfiguration(iteration=1						,	automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__		)
					A__											=					Accelerator(project_dir=SCREAMING_SNAKE_CASE__						,	project_config=SCREAMING_SNAKE_CASE__		)
					A__							,							A__							,							A__							,							A__											=					accelerator.prepare(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE__						,	"checkpoints"						,	"checkpoint_0"		)		)
					((A__)							,							(A__))											=					model.a.item(), model.b.item()
					A__											=					optimizer.state_dict()
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

					A__											=					train(2						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					# Save everything
					accelerator.save_state()

					# Load everything back in and make sure all states work
					accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE__						,	"checkpoints"						,	"checkpoint_1"		)		)
					test_rands += train(1						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					((A__)							,							(A__))											=					model.a.item(), model.b.item()
					A__											=					optimizer.state_dict()
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	str:
				A__											=					torch.tensor([1, 2, 3]		)
				A__											=					torch.tensor([2, 3, 4]		)
				A__											=					DummyModel()
				A__											=					torch.optim.Adam(net.parameters()		)
				A__											=					Accelerator()
				with self.assertRaises(SCREAMING_SNAKE_CASE__		) as ve:
					accelerator.register_for_checkpointing(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				A__											=					str(ve.exception		)
				self.assertTrue("Item at index 0" in message		)
				self.assertTrue("Item at index 1" in message		)
				self.assertFalse("Item at index 2" in message		)
				self.assertFalse("Item at index 3" in message		)


			def   snake_case__	(      self		)      ->	List[str]:
				with tempfile.TemporaryDirectory() as tmpdir:
					set_seed(42		)
					A__											=					DummyModel()
					A__											=					torch.optim.Adam(params=model.parameters()						,	lr=1e-3		)
					A__											=					torch.optim.lr_scheduler.StepLR(SCREAMING_SNAKE_CASE__						,	step_size=1						,	gamma=0.9_9		)
					A__							,							A__											=					dummy_dataloaders()
					A__											=					ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__		)
					# Train baseline
					A__											=					Accelerator(project_dir=SCREAMING_SNAKE_CASE__						,	project_config=SCREAMING_SNAKE_CASE__		)
					A__							,							A__							,							A__							,							A__							,							A__											=					accelerator.prepare(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					# Save initial
					accelerator.save_state()
					A__											=					scheduler.state_dict()
					train(3						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					self.assertNotEqual(SCREAMING_SNAKE_CASE__						,	scheduler.state_dict()		)

					# Load everything back in and make sure all states work
					accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE__						,	"checkpoints"						,	"checkpoint_0"		)		)
					self.assertEqual(SCREAMING_SNAKE_CASE__						,	scheduler.state_dict()		)


			def   snake_case__	(      self		)      ->	List[Any]:
				with tempfile.TemporaryDirectory() as tmpdir:
					set_seed(42		)
					A__											=					DummyModel()
					A__											=					ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__						,	total_limit=2		)
					# Train baseline
					A__											=					Accelerator(project_dir=SCREAMING_SNAKE_CASE__						,	project_config=SCREAMING_SNAKE_CASE__		)
					A__											=					accelerator.prepare(SCREAMING_SNAKE_CASE__		)
					# Save 3 states:
					for _ in range(11		):
						accelerator.save_state()
					self.assertTrue(not os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__						,	"checkpoints"						,	"checkpoint_0"		)		)		)
					self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__						,	"checkpoints"						,	"checkpoint_9"		)		)		)
					self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__						,	"checkpoints"						,	"checkpoint_10"		)		)		)







			@require_cuda
			def   snake_case__	(      self		)      ->	int:
				A__											=					["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
				execute_subprocess_async(SCREAMING_SNAKE_CASE__						,	env=os.environ.copy()		)


if __name__ == "__main__":
			UpperCamelCase     =							"""/tmp/accelerate/state_checkpointing"""
			UpperCamelCase     =							DummyModel()
			UpperCamelCase     =							torch.optim.Adam(params=model.parameters(), lr=1E-3)
			UpperCamelCase     =							torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
			UpperCamelCase  ,					UpperCamelCase     =							dummy_dataloaders()
			UpperCamelCase     =							ProjectConfiguration(automatic_checkpoint_naming=True)
			# Train baseline
			UpperCamelCase     =							Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
			if accelerator.process_index == 0:
						if os.path.exists(savedir):
									shutil.rmtree(savedir)
						os.makedirs(savedir)
			UpperCamelCase  ,					UpperCamelCase  ,					UpperCamelCase  ,					UpperCamelCase  ,					UpperCamelCase     =							accelerator.prepare(
			    model, optimizer, train_dataloader, valid_dataloader, scheduler
			)
			UpperCamelCase  ,					UpperCamelCase     =							accelerator.prepare(model, optimizer)
			train(3, model, train_dataloader, optimizer, accelerator, scheduler)
			# Check that the intial optimizer is loaded on the GPU
			for group in optimizer.param_groups:
						UpperCamelCase     =							group["""params"""][0].device
						break
			assert param_device.type == accelerator.device.type
			UpperCamelCase     =							model.cpu()
			accelerator.wait_for_everyone()
			accelerator.save_state()
			accelerator.wait_for_everyone()

			# Check CPU state
			accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
			for group in optimizer.param_groups:
						UpperCamelCase     =							group["""params"""][0].device
						break
			assert (
			    param_device.type == torch.device("""cpu""").type
			), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

			# Check device state
			model.to(accelerator.device)
			accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
			for group in optimizer.param_groups:
						UpperCamelCase     =							group["""params"""][0].device
						break
			assert (
			    param_device.type == accelerator.device.type
			), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

			# Check error
			with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
						accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
			accelerator.wait_for_everyone()
			if accelerator.process_index == 0:
						shutil.rmtree(savedir)
			accelerator.wait_for_everyone()










"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class        __magic_name__				(			unittest.TestCase		,   UpperCAmelCase__							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   load_tool("""text-classification"""					)
							self.tool.setup()
							lowerCamelCase      =   load_tool("""text-classification"""						,					remote=_a					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.tool("""That's quite cool"""						,					["""positive""", """negative"""]					)
							self.assertEqual(_a						,					"""positive"""					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.remote_tool("""That's quite cool"""						,					["""positive""", """negative"""]					)
							self.assertEqual(_a						,					"""positive"""					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.tool(text="""That's quite cool"""						,					labels=["""positive""", """negative"""]					)
							self.assertEqual(_a						,					"""positive"""					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.remote_tool(text="""That's quite cool"""						,					labels=["""positive""", """negative"""]					)
							self.assertEqual(_a						,					"""positive"""					)



